from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	7.164	0.0	-1	1	NaN	NaN	0.067	0.000	0.166	0.166	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.543	0.0	-1	5	NaN	1.000	0.008	0.000	1.451	1.453	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	6.971	0.0	1	100	NaN	0.875	0.219	0.008	0.052	0.052	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.538	0.0	-1	100	NaN	NaN	0.048	0.000	0.254	0.254	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	6.978	0.0	1	5	NaN	1.000	0.001	0.000	13.461	13.589	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.871	0.0	1	1	NaN	0.975	0.062	0.002	0.188	0.188	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.306	0.0	-1	1	NaN	NaN	0.008	0.000	0.631	0.631	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.317	0.0	-1	5	NaN	1.000	0.001	0.000	6.124	6.209	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.886	0.058	NaN	0.000	0.018	-1	1	0.663	0.687	0.185	0.008	96.588	96.675	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.915	0.046	NaN	0.000	0.002	-1	1	0.663	1.000	0.008	0.000	234.022	234.315	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.325	0.010	NaN	0.000	0.325	-1	1	1.000	NaN	0.047	0.000	6.848	6.848	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.002	NaN	0.000	0.024	-1	1	1.000	0.742	0.182	0.004	0.133	0.133	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.711	0.207	NaN	0.000	0.019	-1	5	0.757	NaN	0.049	0.000	385.125	385.125	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.804	0.063	NaN	0.000	0.003	-1	5	0.757	0.875	0.226	0.006	12.401	12.405	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.316	0.018	NaN	0.000	0.316	-1	5	1.000	0.000	0.008	0.001	38.556	38.639	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.003	NaN	0.000	0.024	-1	5	1.000	NaN	0.046	0.000	0.529	0.529	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.650	0.193	NaN	0.000	0.018	1	100	0.882	0.000	0.008	0.000	2154.097	2157.421	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.119	0.060	NaN	0.000	0.002	1	100	0.882	NaN	0.053	0.000	39.824	39.824	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.318	0.015	NaN	0.000	0.318	1	100	1.000	0.742	0.184	0.003	1.727	1.727	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.001	NaN	0.000	0.022	1	100	1.000	1.000	0.008	0.000	2.561	2.565	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.033	0.137	NaN	0.000	0.018	-1	100	0.882	0.687	0.198	0.011	90.959	91.098	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.832	0.034	NaN	0.000	0.003	-1	100	0.882	1.000	0.008	0.001	340.737	341.405	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.329	0.012	NaN	0.000	0.329	-1	100	1.000	NaN	0.009	0.000	38.082	38.082	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.026	0.003	NaN	0.000	0.026	-1	100	1.000	0.967	0.027	0.002	0.945	0.946	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.589	0.192	NaN	0.000	0.018	1	5	0.757	NaN	0.009	0.000	1919.458	1919.458	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.094	0.043	NaN	0.000	0.002	1	5	0.757	0.974	0.029	0.001	71.946	72.017	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.301	0.012	NaN	0.000	0.301	1	5	1.000	1.000	0.001	0.000	334.934	346.436	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.021	0.000	NaN	0.000	0.021	1	5	1.000	NaN	0.011	0.000	1.997	1.997	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.946	0.026	NaN	0.000	0.018	1	1	0.663	1.000	0.001	0.000	21714.431	22433.240	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.186	0.024	NaN	0.001	0.001	1	1	0.663	NaN	0.008	0.000	141.253	141.253	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.305	0.009	NaN	0.000	0.305	1	1	1.000	0.975	0.063	0.003	4.816	4.824	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.020	0.000	NaN	0.000	0.020	1	1	1.000	1.000	0.001	0.000	21.337	21.629	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	3.836	0.051	NaN	0.000	0.004	-1	1	0.896	0.974	0.031	0.002	122.402	122.624	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	1.724	0.034	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.000	2247.257	2324.638	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.267	0.010	NaN	0.000	0.267	-1	1	1.000	NaN	0.009	0.000	28.782	28.782	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.008	0.003	NaN	0.000	0.008	-1	1	1.000	0.967	0.031	0.001	0.269	0.269	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	2.858	0.0	NaN	0.028	0.0	-1	1	NaN	NaN	0.956	0.000	2.989	2.989	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.833	0.0	NaN	0.021	0.0	-1	5	NaN	1.000	0.000	0.000	9250.339	10107.413	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.775	0.0	NaN	0.021	0.0	1	100	NaN	0.940	0.561	0.011	6.726	6.728	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.660	0.0	NaN	0.022	0.0	-1	100	NaN	NaN	0.702	0.000	5.212	5.212	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.579	0.0	NaN	0.022	0.0	1	5	NaN	1.000	0.000	0.000	30358.468	37342.541	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.756	0.0	NaN	0.021	0.0	1	1	NaN	0.917	0.005	0.001	698.615	702.175	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.002	0.0	NaN	0.007	0.0	-1	1	NaN	NaN	0.006	0.000	0.352	0.352	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	0.023	0.0	-1	5	NaN	1.000	0.000	0.000	6.300	8.066	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	129.649	0.000	NaN	0.000	0.130	-1	1	0.929	0.910	0.107	0.001	1214.241	1214.315	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.532	0.053	NaN	0.000	0.001	-1	1	0.929	1.000	0.000	0.000	1913.338	2103.138	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.706	0.184	NaN	0.000	2.706	-1	1	1.000	NaN	0.773	0.000	3.499	3.499	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.002	NaN	0.000	0.004	-1	1	1.000	0.941	0.189	0.009	0.022	0.022	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	130.282	0.000	NaN	0.000	0.130	-1	5	0.946	NaN	0.727	0.000	179.252	179.252	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.908	0.026	NaN	0.000	0.001	-1	5	0.946	0.940	0.553	0.012	1.641	1.641	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.804	0.110	NaN	0.000	2.804	-1	5	1.000	1.000	0.001	0.000	3642.184	3956.279	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	-1	5	1.000	NaN	0.749	0.000	0.005	0.005	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	133.110	0.000	NaN	0.000	0.133	1	100	0.951	1.000	0.001	0.000	151874.717	159165.853	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	5.269	0.155	NaN	0.000	0.005	1	100	0.951	NaN	0.725	0.000	7.271	7.271	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.658	0.087	NaN	0.000	2.658	1	100	1.000	0.941	0.189	0.004	14.089	14.093	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	1.000	0.000	0.000	7.225	7.743	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	131.252	0.000	NaN	0.000	0.131	-1	100	0.951	0.910	0.102	0.002	1287.355	1287.668	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	2.837	0.055	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	8758.045	9601.973	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.692	0.137	NaN	0.000	2.692	-1	100	1.000	NaN	0.001	0.000	2375.317	2375.317	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.005	0.002	NaN	0.000	0.005	-1	100	1.000	0.879	0.000	0.000	10.651	10.994	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	136.236	0.000	NaN	0.000	0.136	1	5	0.946	NaN	0.004	0.000	38681.631	38681.631	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.536	0.031	NaN	0.000	0.002	1	5	0.946	0.905	0.001	0.000	1864.415	1953.008	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.847	0.157	NaN	0.000	2.847	1	5	1.000	1.000	0.000	0.000	22801.402	28733.141	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	NaN	0.006	0.000	0.286	0.286	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	133.334	0.000	NaN	0.000	0.133	1	1	0.929	1.000	0.000	0.000	1080394.548	1328266.609	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.817	0.015	NaN	0.000	0.001	1	1	0.929	NaN	0.005	0.000	163.813	163.813	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.769	0.094	NaN	0.000	2.769	1	1	1.000	0.917	0.005	0.000	508.309	510.179	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	8.250	10.107	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.041	0.004	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	48.043	49.602	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.025	0.002	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	206.009	254.027	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.005	0.001	NaN	0.000	0.005	-1	1	1.000	NaN	0.004	0.000	1.359	1.359	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.002	0.001	NaN	0.000	0.002	-1	1	1.000	0.879	0.001	0.000	4.386	4.522	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.855	0.0	30	0.019	0.0	random	NaN	30	NaN	0.443	0.0	1.931	1.931	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.603	0.0	30	0.027	0.0	k-means++	NaN	30	NaN	0.459	0.0	1.314	1.314	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	6.067	0.0	30	0.132	0.0	random	NaN	30	NaN	2.861	0.0	2.121	2.121	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	6.032	0.0	30	0.133	0.0	k-means++	NaN	30	NaN	2.942	0.0	2.050	2.050	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.002	0.0	30	0.010	0.000	random	0.000	30	0.001	0.0	0.0	8.506	9.723	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	random	1.000	30	1.000	0.0	0.0	11.078	13.101	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.002	0.0	30	0.010	0.000	k-means++	0.001	30	0.001	0.0	0.0	7.569	9.263	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.002	0.0	30	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	12.719	15.584	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.394	0.000	random	0.002	30	0.002	0.0	0.0	6.727	7.104	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.002	0.0	30	0.001	0.002	random	1.000	30	1.000	0.0	0.0	10.828	12.580	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.448	0.000	k-means++	0.002	30	0.002	0.0	0.0	5.908	6.406	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.002	0.0	30	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	9.203	11.517	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.113	0.0	20	0.001	0.0	random	NaN	20	NaN	0.055	0.0	2.059	2.059	See	See
3	KMeans_short	False	fit	10000	10000	2	0.238	0.0	20	0.001	0.0	k-means++	NaN	20	NaN	0.091	0.0	2.600	2.600	See	See
6	KMeans_short	False	fit	10000	10000	100	0.379	0.0	20	0.021	0.0	random	NaN	20	NaN	0.131	0.0	2.887	2.887	See	See
9	KMeans_short	False	fit	10000	10000	100	0.648	0.0	20	0.012	0.0	k-means++	NaN	20	NaN	0.312	0.0	2.074	2.074	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.006	0.014	20	0.003	0.000	random	0.002	20	0.001	0.001	0.001	6.200	7.891	See	See
2	KMeans_short	False	predict	10000	1	2	0.005	0.008	20	0.000	0.005	random	1.000	20	1.000	0.000	0.000	30.545	35.293	See	See
4	KMeans_short	False	predict	10000	1000	2	0.002	0.000	20	0.009	0.000	k-means++	0.002	20	0.001	0.001	0.000	2.938	3.037	See	See
5	KMeans_short	False	predict	10000	1	2	0.003	0.004	20	0.000	0.003	k-means++	1.000	20	1.000	0.000	0.000	22.135	25.061	See	See
7	KMeans_short	False	predict	10000	1000	100	0.003	0.001	20	0.272	0.000	random	0.321	20	0.311	0.001	0.000	2.194	2.248	See	See
8	KMeans_short	False	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	1.000	20	1.000	0.000	0.000	9.106	10.279	See	See
10	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	0.249	0.000	k-means++	0.381	20	0.286	0.001	0.001	2.328	2.512	See	See
11	KMeans_short	False	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.000	8.698	9.362	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	11.800	0.0	[20]	0.068	0.000	NaN	NaN	NaN	NaN	NaN	1.926	0.0	6.127	6.127	See	See
3	LogisticRegression	False	fit	1000	1000	10000	6.147	0.0	[26]	0.013	0.006	NaN	NaN	NaN	NaN	NaN	0.811	0.0	7.577	7.577	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.000	0.0	[20]	2.705	0.0	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.775	0.834	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	0.011	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.355	0.395	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	3.476	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.0	0.700	0.705	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	0.756	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.122	0.126	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.194	0.0	NaN	0.412	0.0	NaN	NaN	NaN	0.208	0.0	0.932	0.932	See	See
3	Ridge	False	fit	1000000	1000000	100	1.459	0.0	NaN	0.548	0.0	NaN	NaN	NaN	0.924	0.0	1.579	1.579	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.01	0.0	NaN	7.661	0.0	NaN	NaN	0.083	0.017	0.0	0.615	0.615	See	See
2	Ridge	False	predict	1000	1	10000	0.00	0.0	NaN	1.045	0.0	NaN	NaN	NaN	0.000	0.0	0.575	0.688	See	See
4	Ridge	False	predict	1000000	1000	100	0.00	0.0	NaN	3.812	0.0	NaN	NaN	1.000	0.000	0.0	0.795	0.876	See	See
5	Ridge	False	predict	1000000	1	100	0.00	0.0	NaN	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.680	0.792	See	See